Dynamics of the QTc interval over a 24-h dose interval after start of intravenous ciprofloxacin or low-dose erythromycin administration in ICU patients.

QTc interval prolongation is an adverse effect associated with the use of fluoroquinolones and macrolides. Ciprofloxacin and erythromycin are both frequently prescribed QTc-prolonging drugs in critically ill patients. Critically ill patients may be more vulnerable to developing QTc prolongation, as several risk factors can be present at the same time. Therefore, it is important to know the QTc-prolonging potential of these drugs in the intensive care unit (ICU) population. The aim of this study was to assess the dynamics of the QTc interval over a 24-hour dose interval during intravenous ciprofloxacin and low-dose erythromycin treatment. Therefore, an observational study was performed in ICU patients (≥18 years) receiving ciprofloxacin 400 mg t.i.d. or erythromycin 100 mg b.i.d. intravenously. Continuous ECG data were collected from 2 h before to 24 h after the first administration. QT-analyses were performed using high-end holter software. The effect was determined with a two-sample t-test for clustered data on all QTc values. A linear mixed model by maximum likelihood was applied, for which QTc values were assessed for the available time intervals and therapy. No evident effect over time on therapy with ciprofloxacin and erythromycin was observed on QTc time. There was no significant difference (p = 0.22) in QTc values between the ciprofloxacin group (mean 393 ms) and ciprofloxacin control group (mean 386 ms). The erythromycin group (mean 405 ms) and erythromycin control group (mean 404 ms) neither showed a significant difference (p = 0.80). In 0.6% of the registrations (1.138 out of 198.270 samples) the duration of the QTc interval was longer than 500 ms. The index groups showed slightly more recorded QTc intervals over 500 ms. To conclude, this study could not identify differences in the QTc interval between the treatments analyzed.

The use of a clinical decision support tool to assess the risk of QT drug-drug interactions in community pharmacies.

INTRODUCTION: The handling of drug-drug interactions regarding QTc-prolongation (QT-DDIs) is not well defined. A clinical decision support (CDS) tool will support risk management of QT-DDIs. Therefore, we studied the effect of a CDS tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. METHODS: An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of three months were included. The impact of the use of a CDS tool to support the handling of QT-DDIs was studied. For each QT-DDI, handling of the QT-DDI and patient characteristics were extracted from the pharmacy information system. Primary outcome was the proportion of QT-DDIs with an intervention. Secondary outcomes were the type of interventions and the time associated with handling QT-DDIs. Logistic regression analysis was used to analyse the primary outcome. RESULTS: Two hundred and forty-four QT-DDIs pre-CDS tool and 157 QT-DDIs post-CDS tool were included. Pharmacists intervened in 43.0% and 35.7% of the QT-DDIs pre- and post-CDS tool respectively (odds ratio 0.74; 95% confidence interval 0.49-1.11). Substitution of interacting agents was the most frequent intervention. Pharmacists spent 20.8 ± 3.5 min (mean ± SD) on handling QT-DDIs pre-CDS tool, which was reduced to 14.9 ± 2.4 min (mean ± SD) post-CDS tool. Of these, 4.5 ± 0.7 min (mean ± SD) were spent on the CDS tool. CONCLUSION: The CDS tool might be a first step to developing a tool to manage QT-DDIs via a structured approach. Improvement of the tool is needed in order to increase its diagnostic value and reduce redundant QT-DDI alerts. PLAIN LANGUAGE SUMMARY: The use of a tool to support the handling of QTc-prolonging drug interactions in community pharmacies Introduction: Several drugs have the ability to cause heart rhythm disturbances as a rare side effect. This rhythm disturbance is called QTc-interval prolongation. It may result in cardiac arrest. For health care professionals, such as physicians and pharmacists, it is difficult to decide whether or not it is safe to proceed treating a patient with combinations of two or more of these QT-prolonging drugs. Recently, a tool was developed that supports the risk management of these QT drug-drug interactions (QT-DDIs).Methods: In this study, we studied the effect of this tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of 3 months were included.Results: Two hundred and forty-four QT-DDIs pre-implementation of the tool and 157 QT-DDIs post-implementation of the tool were included. Pharmacists intervened in 43.0% of the QT-DDIs before the tool was implemented and in 35.7% after implementation of the tool. Substitution of one of the interacting agents was the most frequent intervention. Pharmacists spent less time on handling QT-DDIs when the tool was used.Conclusion: The clinical decision support tool might be a first step to developing a tool to manage QT-DDIs via a structured approach.

Comparison of two algorithms to support medication surveillance for drug-drug interactions between QTc-prolonging drugs.

BACKGROUND: QTc-prolongation is an independent risk factor for developing life-threatening arrhythmias. Risk management of drug-induced QTc-prolongation is complex and digital support tools could be of assistance. Bindraban et al. and Berger et al. developed two algorithms to identify patients at risk for QTc-prolongation. OBJECTIVE: The main aim of this study was to compare the performances of these algorithms for managing QTc-prolonging drug-drug interactions (QT-DDIs). MATERIALS AND METHODS: A retrospective data analysis was performed. A dataset was created from QT-DDI alerts generated for in- and outpatients at a general teaching hospital between November 2016 and March 2018. ECGs recorded within 7 days of the QT-DDI alert were collected. Main outcomes were the performance characteristics of both algorithms. QTc-intervals of > 500 ms on the first ECG after the alert were taken as outcome parameter, to which the performances were compared. Secondary outcome was the distribution of risk scores in the study cohort. RESULTS: In total, 10,870 QT-DDI alerts of 4987 patients were included. ECGs were recorded in 26.2 % of the QT-DDI alerts. Application of the algorithms resulted in area under the ROC-curves of 0.81 (95 % CI 0.79-0.84) for Bindraban et al. and 0.73 (0.70-0.75) for Berger et al. Cut-off values of ≥ 3 and ≥ 6 led to sensitivities of 85.7 % and 89.1 %, and specificities of 60.8 % and 44.3 % respectively. CONCLUSIONS: Both algorithms showed good discriminative abilities to identify patients at risk for QTc-prolongation when using ≥ 2 QTc-prolonging drugs. Implementation of digital algorithms in clinical decision support systems could support the risk management of QT-DDIs.

Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice.

BACKGROUND: The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs). A tool to identify high-risk patients is needed to support healthcare providers in handling automatically generated alerts in clinical practice. The main aim of this study was to develop and validate a tool to assess the risk of QT-DDIs in clinical practice. METHODS: A model was developed based on risk factors associated with QTc-prolongation determined in a prospective study on QT-DDIs in a university medical center inthe Netherlands. The main outcome measure was QTc-prolongation defined as a QTc interval > 450 ms for males and > 470 ms for females. Risk points were assigned to risk factors based on their odds ratios. Additional risk factors were added based on a literature review. The ability of the model to predict QTc-prolongation was validated in an independent dataset obtained from a general teaching hospital against QTc-prolongation as measured by an ECG as the gold standard. Sensitivities, specificities, false omission rates, accuracy and Youden's index were calculated. RESULTS: The model included age, gender, cardiac comorbidities, hypertension, diabetes mellitus, renal function, potassium levels, loop diuretics, and QTc-prolonging drugs as risk factors. Application of the model to the independent dataset resulted in an area under the ROC-curve of 0.54 (95% CI 0.51-0.56) when QTc-prolongation was defined as > 450/470 ms, and 0.59 (0.54-0.63) when QTc-prolongation was defined as > 500 ms. A cut-off value of 6 led to a sensitivity of 76.6 and 83.9% and a specificity of 28.5 and 27.5% respectively. CONCLUSIONS: A clinical decision support tool with fair performance characteristics was developed. Optimization of this tool may aid in assessing the risk associated with QT-DDIs. TRIAL REGISTRATION: No trial registration, MEC-2015-368.

The Risk of QTc-Interval Prolongation in Breast Cancer Patients Treated with Tamoxifen in Combination with Serotonin Reuptake Inhibitors.

PURPOSE: Antidepressants like the serotonin reuptake inhibitors (SRIs) are often used concomitantly with tamoxifen (e.g. for treatment of depression). This may lead to an additional prolongation of the QTc-interval, with an increased risk of cardiac side effects. Therefore we investigated whether there is a drug-drug interaction between tamoxifen and SRIs resulting in a prolonged QTc-interval. METHODS: Electrocardiograms (ECGs) of 100 patients were collected at steady state tamoxifen treatment, with or without concomitant SRI co-medication. QTc-interval was manually measured and calculated using the Fridericia formula. Primary outcome was difference in QTc-interval between tamoxifen monotherapy and tamoxifen concomitantly with an SRI. RESULTS: The mean QTc-interval was 12.4 ms longer when tamoxifen was given concomitantly with an SRI (95% CI:1.8-23.1 ms; P = 0.023). Prolongation of the QTc-interval was particularly pronounced for paroxetine (17.2 ms; 95%CI:1.4-33.0 ms; P = 0.04), escitalopram (12.5 ms; 95%CI:4.4-20.6 ms; P < 0.01) and citalopram (20.7 ms; 95%CI:0.7-40.7 ms; P = 0.047), where other agents like venlafaxine did not seem to prolong the QTc-interval. None of the patients had a QTc-interval of >500 ms. CONCLUSIONS: Concomitant use of tamoxifen and SRIs resulted in a significantly higher mean QTc-interval, which was especially the case for paroxetine, escitalopram and citalopram. When concomitant administration with an SRI is warranted venlafaxine is preferred.

Differences in CYP3A genotypes of a liver transplant recipient and the donor liver graft and adjustment of tacrolimus dose.

Tacrolimus (Tac) is well established as main immunosuppressant in most immunosuppressive regimens in solid organ transplantation. Due to the narrow therapeutic window, pre dose Tac levels (C0) are monitored in all patients receiving Tac to reach optimal therapeutic levels. Tac is metabolized in the liver and intestine by the cytochrome P450 3A (CYP3A) isoforms CYP3A4 and CYP3A5. We present a case of an African American woman who underwent a liver transplantation in which adequate Tac levels were difficult to accomplish due to differences in cytochrome P450 3A4/5 (CYP3A4/5) polymorphisms of the transplant recipient and the donor liver graft. This case report highlights that genotyping the liver transplant recipient and the donor liver graft might provide data which could be used to predict the tacrolimus metabolism post transplantation.

Development of a risk model for predicting QTc interval prolongation in patients using QTc-prolonging drugs.

Background Numerous drugs prolong the QTc interval on the ECG and potentially increase the risk of cardiac arrhythmia. This risk is clinically relevant in patients with additional risk factors. Objective The objective was to develop and validate a risk model to predict QTc interval prolongation of eligible ECGs. Setting Spaarne Gasthuis (Haarlem/Hoofddorp, The Netherlands). Method A dataset was created from ECGs recorded in patients using one or more QTc prolonging drugs, in the period January 2013 and October 2016. In the development set, independent risk factors for QTc interval prolongation were determined using binary logistic regression. Risk scores were assigned based on the beta coefficient. In the risk-score validation set, the area under the ROC-curve, sensitivity and specificity were calculated. Main outcome measure QTc interval prolongation, defined as a QTc interval > 500 ms. Results In the development set 12,949 ECGs were included and in the risk-score validation set 6391 ECGs. The proportion of ECGs with a prolonged QTc interval in patients with no risk factors in the risk-score validation set was 2.7%, while in patients with a high risk score the proportion was 26.1%. The area under the ROC curve was 0.71 (95% CI 0.68-0.73). The sensitivity and specificity were 0.81 and 0.48, respectively. Conclusion A risk model was developed and validated for the prediction of QTc interval prolongation. This risk model can be implemented in a clinical decision support system, supporting the management of the risks involved with QTc interval prolonging drugs.

QTc prolongation during ciprofloxacin and fluconazole combination therapy: prevalence and associated risk factors.

AIM(S): Ciprofloxacin and fluconazole combination therapy is frequently used as prophylaxis for, and treatment of, infections in patients with haematological malignancies. However, both drugs are known to prolong the heart rate-corrected QT (QTc) interval, which is a serious risk factor for torsade de pointes (TdP). Therefore, the aim of the current study was to assess the prevalence of QTc prolongation during ciprofloxacin and fluconazole use. The secondary objective was to determine associated risk factors of QTc prolongation in these patients. METHODS: A prospective observational study was performed in patients admitted to the Erasmus University Medical Centre and treated with ciprofloxacin and fluconazole. A 12-lead electrocardiogram (ECG) was recorded at the estimated time to peak concentration (Tmax ) for the last added drug. The main outcome was the proportion of patients with QTc prolongation during treatment. Data on the following potential risk factors were collected: patient characteristics, serum electrolyte levels, dosage of ciprofloxacin and fluconazole, renal and liver function and concomitant use of other QTc-prolonging drugs and cytochrome P450 3A4 inhibitors. RESULTS: A total of 170 patients were included, of whom 149 (87.6%) were treated for haematological malignancies. The prevalence of QTc prolongation was 4.7%. No risk factors were found to be associated with QTc prolongation. The QTc interval increased by 10.7 ms [95% confidence interval (CI) 7.2, 14.1 ms] during ciprofloxacin and fluconazole combination therapy. CONCLUSION: The prevalence of QTc prolongation in patients using ciprofloxacin and fluconazole is low compared with the prevalence in the general population, which varies from 5% to 11%. In addition, no risk factors were found. Given the low prevalence, routine ECG monitoring in patients on this therapy should be reconsidered.

Risk factors for QTc interval prolongation.

PURPOSE: Prolongation of the QTc interval may result in Torsade de Pointes, a ventricular arrhythmia. Numerous risk factors for QTc interval prolongation have been described, including the use of certain drugs. In clinical practice, there is much debate about the management of the risks involved. In this study, we quantified the effect of these risk factors on the length of the QTc interval. METHODS: We analyzed all ECGs that were taken during routine practice between January 2013 and October 2016 in the Spaarne Gasthuis, a general teaching hospital in the Netherlands. We collected laboratory values in the week before the ECG recording and the drugs prescribed. For the identification of risk factors, we used multilevel linear regression analysis to correct for multiple ECG recordings per patient. RESULTS: We included 133,359 ECGs in our study, taken in 40,037 patients. Patients using one QT-prolonging drug had a 11.08 ms (95% CI 10.63-11.52; p < 0.001) longer QTc interval. Patients using two QT-prolonging drugs had a 3.04 ms (95% CI 2.06-4.02; p < 0.001) increase in the QTc interval compared to patients using one QT-prolonging drug. Women had a longer QTc interval compared to men (16.30 ms 95% CI 14.59-18.01; p < 0.001). The QTc interval increased with increasing age, but the difference between men and women diminished. Other independent risk factors that significantly prolonged the QTc interval with at least 10 ms were hypokalemia, hypocalcemia, and the use of loop diuretics. CONCLUSION: We identified and quantified various risk factors for QTc interval prolongation.